{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch the data and load it in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "url = (\"https://archive.ics.uci.edu/ml/machine-learning-databases\"\n",
    "       \"/adult/adult.data\")\n",
    "local_filename = os.path.basename(url)\n",
    "if not os.path.exists(local_filename):\n",
    "    print(\"Downloading Adult Census datasets from UCI\")\n",
    "    urlretrieve(url, local_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = (\"age, workclass, fnlwgt, education, education-num, \"\n",
    "         \"marital-status, occupation, relationship, race, sex, \"\n",
    "         \"capital-gain, capital-loss, hours-per-week, \"\n",
    "         \"native-country, income\").split(', ')    \n",
    "data = pd.read_csv(local_filename, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               32561\n",
       "workclass         32561\n",
       "fnlwgt            32561\n",
       "education         32561\n",
       "education-num     32561\n",
       "marital-status    32561\n",
       "occupation        32561\n",
       "relationship      32561\n",
       "race              32561\n",
       "sex               32561\n",
       "capital-gain      32561\n",
       "capital-loss      32561\n",
       "hours-per-week    32561\n",
       "native-country    32561\n",
       "income            32561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " ?                    1843\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('occupation').size().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "native-country\n",
       " United-States    29170\n",
       " Mexico             643\n",
       " ?                  583\n",
       " Philippines        198\n",
       " Germany            137\n",
       " Canada             121\n",
       " Puerto-Rico        114\n",
       " El-Salvador        106\n",
       " India              100\n",
       " Cuba                95\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('native-country').size().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(column='education-num', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(column='age', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gnz8hbWZmGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZWcbhYGZmmTGHg6Q3S3q49Nov6SOSPiFpV6n87NI+V0jqkfSEpDNL5fNTWY+kFfUOyszM6jN5rDtGxBPAPABJk4BdwB3AhcC1EfGZcn1JxwOLgBOAY4BvSDoubf488C6gF3hQ0rqIeGysfTMzs/qMORwGOR14KiKeljRcnQXA2oh4CfiepB7g5LStJyK2A0ham+o6HMzMmqRR4bAIuLm0vkzSBcBmYHlEPAdMBzaV6vSmMoCdg8pPGeogkpYCSwEqlQrVarXmDvb19Y2qfrtolXEvn9vf0PYqU2iJcTdaq5zvRuvUccP4jb3ucJB0EPAe4IpUtBK4Coj0/lngonqPAxARq4BVAF1dXdHd3V3zvtVqldHUbxetMu4lK+5uaHvL5/azsAXG3Witcr4brVPHDeM39kZcOZwFfCci9gAMvANI+iJwV1rdBcws7TcjlXGAcjMza4JGPMp6HqUpJUnTStveC2xNy+uARZIOljQbmAN8G3gQmCNpdroKWZTqmplZk9R15SDpEIqnjD5UKv5zSfMoppV2DGyLiG2SbqW40dwPXBYRP0/tLAPuASYBqyNiWz39MjOz+tQVDhHxAnDkoLLzD1D/auDqIcrXA+vr6YuZmTWOPyFtZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZpu5wkLRD0qOSHpa0OZUdIWmDpCfT+9RULknXSeqR9Iikt5TaWZzqPylpcb39MjOzsWvUlcNpETEvIrrS+gpgY0TMATamdYCzgDnptRRYCUWYAFcCpwAnA1cOBIqZmb36xmtaaQGwJi2vAc4tld8YhU3A4ZKmAWcCGyJiX0Q8B2wA5o9T38zMbASTG9BGAF+XFMD/iYhVQCUidqftzwCVtDwd2FnatzeVDVf+CpKWUlxxUKlUqFarNXeyr69vVPXbRauMe/nc/oa2V5lCS4y70VrlfDdap44bxm/sjQiHt0XELkm/CmyQ9N3yxoiIFBx1S8GzCqCrqyu6u7tr3rdarTKa+u2iVca9ZMXdDW1v+dx+FrbAuButVc53o3XquGH8xl73tFJE7Erve4E7KO4Z7EnTRaT3van6LmBmafcZqWy4cjMza4K6wkHSIZIOG1gGzgC2AuuAgSeOFgN3puV1wAXpqaVTgefT9NM9wBmSpqYb0WekMjMza4J6p5UqwB2SBtr6ckT8o6QHgVslXQw8DSxM9dcDZwM9wIvAhQARsU/SVcCDqd4nI2JfnX0zM7MxqiscImI78JtDlD8LnD5EeQCXDdPWamB1Pf0xM7PG8Cekzcws43AwM7OMw8HMzDIOBzMzyzgczMws43AwM7OMw8HMzDIOBzMzyzgczMws43AwM7OMw8HMzDKN+D0Hs44wq8G/ObHjmnc3tD2zRvKVg5mZZRwOZmaWcTiYmVnG4WBmZhmHg5mZZRwOZmaWcTiYmVnG4WBmZhmHg5mZZcYcDpJmSrpX0mOStkm6PJV/QtIuSQ+n19mlfa6Q1CPpCUlnlsrnp7IeSSvqG5KZmdWrnq/P6AeWR8R3JB0GbJG0IW27NiI+U64s6XhgEXACcAzwDUnHpc2fB94F9AIPSloXEY/V0TczM6vDmMMhInYDu9PyjyU9Dkw/wC4LgLUR8RLwPUk9wMlpW09EbAeQtDbVdTiYmTWJIqL+RqRZwP3AicAfA0uA/cBmiquL5yT9DbApIv4+7XM98LXUxPyI+GAqPx84JSKWDXGcpcBSgEqlctLatWtr7mNfXx+HHnroWIbX0lpl3I/uer6h7VWmwJ6fNLTJhps7/fUNb7NVznejdeq4YfRjP+2007ZERNdI9er+VlZJhwK3Ax+JiP2SVgJXAZHePwtcVO9xACJiFbAKoKurK7q7u2vet1qtMpr67aJVxr2kwd94unxuP599dGJ/6fCO93c3vM1WOd+N1qnjhvEbe13/75H0GopguCkivgIQEXtK278I3JVWdwEzS7vPSGUcoNzMzJqgnqeVBFwPPB4Rf1kqn1aq9l5ga1peByySdLCk2cAc4NvAg8AcSbMlHURx03rdWPtlZmb1q+fK4a3A+cCjkh5OZR8DzpM0j2JaaQfwIYCI2CbpVoobzf3AZRHxcwBJy4B7gEnA6ojYVke/zMysTvU8rfTPgIbYtP4A+1wNXD1E+foD7WdmZq8uf0LazMwyE/txjg7WqN8rXj63nyUr7vbvFZvZqPjKwczMMr5yMGsjj+56vqGfGfEVZ+fylYOZmWV85WBj0qh7ImY2MfnKwczMMg4HMzPLOBzMzCzjcDAzs4zDwczMMn5aqUP46SIzGw1fOZiZWcZXDmZNMh5Xc8vnNrxJ61C+cjAzs4zDwczMMp5WagDf7DWzduMrBzMzyzgczMws42klMxtWo6dM/fsQraMjw8H3CMzMDmzChIOk+cDngEnAlyLimiZ3ycxawKwVd7/8W+mN4KubwoS45yBpEvB54CzgeOA8Scc3t1dmZp1rolw5nAz0RMR2AElrgQXAY03tlZk1VCtM6U70Pr5aVzaKiFflQAfshPQ+YH5EfDCtnw+cEhHLBtVbCixNq28GnhjFYY4CftiA7rYaj7uzeNydZ7RjPzYijh6p0kS5cqhJRKwCVo1lX0mbI6KrwV2a8DzuzuJxd57xGvuEuOcA7AJmltZnpDIzM2uCiRIODwJzJM2WdBCwCFjX5D6ZmXWsCTGtFBH9kpYB91A8yro6IrY1+DBjmo5qAx53Z/G4O8+4jH1C3JA2M7OJZaJMK5mZ2QTicDAzs0zbh4Ok+ZKekNQjaUWz+zNeJM2UdK+kxyRtk3R5Kj9C0gZJT6b3qc3u63iQNEnSQ5LuSuuzJT2Qzvst6UGHtiPpcEm3SfqupMcl/U4nnHNJ/y39d75V0s2SfqUdz7mk1ZL2StpaKhvy/KpwXRr/I5LeUs+x2zocOuxrOfqB5RFxPHAqcFka6wpgY0TMATam9XZ0OfB4af3TwLUR8SbgOeDipvRq/H0O+MeI+HXgNyn+N2jrcy5pOvBHQFdEnEjxEMsi2vOc3wDMH1Q23Pk9C5iTXkuBlfUcuK3DgdLXckTET4GBr+VoOxGxOyK+k5Z/TPFHYjrFeNekamuAc5vTw/EjaQbwbuBLaV3AO4DbUpV2HffrgbcD1wNExE8j4kd0wDmneNJyiqTJwGuB3bThOY+I+4F9g4qHO78LgBujsAk4XNK0sR673cNhOrCztN6bytqapFnAbwEPAJWI2J02PQNUmtSt8fRXwEeBX6T1I4EfRUR/Wm/X8z4b+AHwd2lK7UuSDqHNz3lE7AI+A/wbRSg8D2yhM845DH9+G/r3rt3DoeNIOhS4HfhIROwvb4viueW2enZZ0jnA3ojY0uy+NMFk4C3Ayoj4LeAFBk0htek5n0rxr+TZwDHAIeRTLx1hPM9vu4dDR30th6TXUATDTRHxlVS8Z+DSMr3vbVb/xslbgfdI2kExbfgOinn4w9OUA7Tvee8FeiPigbR+G0VYtPs5fyfwvYj4QUT8DPgKxX8HnXDOYfjz29C/d+0eDh3ztRxpnv164PGI+MvSpnXA4rS8GLjz1e7beIqIKyJiRkTMoji//xQR7wfuBd6XqrXduAEi4hlgp6Q3p6LTKb7mvq3POcV00qmSXpv+ux8Yd9uf82S487sOuCA9tXQq8Hxp+mnU2v4T0pLOppiTHvhajqub3KVxIeltwDeBR/nl3PvHKO473Aq8AXgaWBgRg29wtQVJ3cB/j4hzJL2R4kriCOAh4AMR8VIz+zceJM2juBF/ELAduJDiH31tfc4l/W/gDyie0nsI+CDF/HpbnXNJNwPdFF/LvQe4EvgqQ5zfFJR/QzHF9iJwYURsHvOx2z0czMxs9Np9WsnMzMbA4WBmZhmHg5mZZRwOZmaWcTiYmVnG4WBmZhmHg5mZZf4/7u8CCfGyNg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist('hours-per-week', bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='age', y='hours-per-week', kind='scatter',\n",
    "          alpha=0.02, s=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income\n",
       " <=50K    24720\n",
       " >50K      7841\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('income')['income'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2408095574460244"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data['income'] == ' >50K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_names = data['income'].unique()\n",
    "target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low_income = data[data['income'] == ' <=50K']\n",
    "high_income = data[data['income'] == ' >50K']\n",
    "\n",
    "bins = np.linspace(10, 90, 20)\n",
    "plt.hist(low_income['age'].values, bins=bins, alpha=0.5, label='<=50K')\n",
    "plt.hist(high_income['age'].values, bins=bins, alpha=0.5, label='>50K')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(low_income['age'], low_income['hours-per-week'],\n",
    "            alpha=0.03, s=50, c='b', label='<=50K');\n",
    "plt.scatter(high_income['age'], high_income['hours-per-week'],\n",
    "            alpha=0.03, s=50, c='g', label='>50K');\n",
    "plt.legend()\n",
    "plt.xlabel('age'); plt.ylabel('hours-per-week');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering for predictive models\n",
    "\n",
    "### Manual feature engineering with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target = data['income']\n",
    "features_data = data.drop('income', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'fnlwgt',\n",
       " 'education-num',\n",
       " 'capital-gain',\n",
       " 'capital-loss',\n",
       " 'hours-per-week']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = [c for c in features_data if features_data[c].dtype.kind in ('i', 'f')]\n",
    "numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week\n",
       "0   39   77516             13          2174             0              40\n",
       "1   50   83311             13             0             0              13\n",
       "2   38  215646              9             0             0              40\n",
       "3   53  234721              7             0             0              40\n",
       "4   28  338409             13             0             0              40"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data = features_data[numeric_features]\n",
    "numeric_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['workclass',\n",
       " 'education',\n",
       " 'marital-status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'native-country']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = [c for c in features_data if features_data[c].dtype.kind not in ('i', 'f')]\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           workclass   education       marital-status          occupation  \\\n",
       "0          State-gov   Bachelors        Never-married        Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse     Exec-managerial   \n",
       "2            Private     HS-grad             Divorced   Handlers-cleaners   \n",
       "3            Private        11th   Married-civ-spouse   Handlers-cleaners   \n",
       "4            Private   Bachelors   Married-civ-spouse      Prof-specialty   \n",
       "\n",
       "     relationship    race      sex  native-country  \n",
       "0   Not-in-family   White     Male   United-States  \n",
       "1         Husband   White     Male   United-States  \n",
       "2   Not-in-family   White     Male   United-States  \n",
       "3         Husband   Black     Male   United-States  \n",
       "4            Wife   Black   Female            Cuba  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data = features_data[categorical_features]\n",
    "categorical_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>occupation</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass  education  marital-status  occupation  relationship  race  sex  \\\n",
       "0          0          0               0           0             0     0    0   \n",
       "1          1          0               1           1             1     0    0   \n",
       "2          2          1               2           2             0     0    0   \n",
       "3          2          2               1           2             1     1    0   \n",
       "4          2          0               1           3             2     1    1   \n",
       "\n",
       "   native-country  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data_encoded = categorical_data.apply(lambda x: pd.factorize(x)[0])\n",
    "categorical_data_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass  education  marital-status  occupation  relationship  race  sex  \\\n",
       "0          0          0               0           0             0     0    0   \n",
       "1          1          0               1           1             1     0    0   \n",
       "2          2          1               2           2             0     0    0   \n",
       "3          2          2               1           2             1     1    0   \n",
       "4          2          0               1           3             2     1    1   \n",
       "\n",
       "   native-country  age  fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "0               0   39   77516             13          2174             0   \n",
       "1               0   50   83311             13             0             0   \n",
       "2               0   38  215646              9             0             0   \n",
       "3               0   53  234721              7             0             0   \n",
       "4               1   28  338409             13             0             0   \n",
       "\n",
       "   hours-per-week  \n",
       "0              40  \n",
       "1              13  \n",
       "2              40  \n",
       "3              40  \n",
       "4              40  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.concat([categorical_data_encoded, numeric_data], axis=1)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Alternatively: use one-hot encoding for categorical features\n",
    "# features = pd.get_dummies(features_data)\n",
    "# features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = features.values.astype(np.float32)\n",
    "y = (target.values == ' >50K').astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 14)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.1740e+03, 0.0000e+00,\n",
       "        4.0000e+01],\n",
       "       [1.0000e+00, 0.0000e+00, 1.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "        1.3000e+01],\n",
       "       [2.0000e+00, 1.0000e+00, 2.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "        4.0000e+01],\n",
       "       ...,\n",
       "       [2.0000e+00, 1.0000e+00, 6.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "        4.0000e+01],\n",
       "       [2.0000e+00, 1.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n",
       "        2.0000e+01],\n",
       "       [6.0000e+00, 1.0000e+00, 1.0000e+00, ..., 1.5024e+04, 0.0000e+00,\n",
       "        4.0000e+01]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "\n",
    "feature_preprocessor = make_column_transformer(\n",
    "    (categorical_features, OrdinalEncoder()),\n",
    "    remainder='passthrough'\n",
    ")\n",
    "\n",
    "X = feature_preprocessor.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 14)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a first Decision Tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "features_train, features_test, X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26048, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6513, 14)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Decision Tree: 0.8969 +/-0.0033\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc')\n",
    "print(\"ROC AUC Decision Tree: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model error analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator, X, y, ylim=(0, 1.1), cv=5,\n",
    "                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5),\n",
    "                        scoring=None):\n",
    "    plt.title(\"Learning curves for %s\" % type(estimator).__name__)\n",
    "    plt.ylim(*ylim); plt.grid()\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, validation_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\n",
    "        scoring=scoring)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    validation_scores_mean = np.mean(validation_scores, axis=1)\n",
    "\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    print(\"Best validation score: {:.4f}\".format(validation_scores_mean[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.7492\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=None)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8568\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=15)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8967\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best validation score: 0.8636\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=4)\n",
    "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "\n",
    "def plot_validation_curve(estimator, X, y, param_name, param_range,\n",
    "                          ylim=(0, 1.1), cv=5, n_jobs=-1, scoring=None):\n",
    "    estimator_name = type(estimator).__name__\n",
    "    plt.title(\"Validation curves for %s on %s\"\n",
    "              % (param_name, estimator_name))\n",
    "    plt.ylim(*ylim); plt.grid()\n",
    "    plt.xlim(min(param_range), max(param_range))\n",
    "    plt.xlabel(param_name)\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    train_scores, test_scores = validation_curve(\n",
    "        estimator, X, y, param_name, param_range,\n",
    "        cv=cv, n_jobs=n_jobs, scoring=scoring)\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    plt.semilogx(param_range, train_scores_mean, 'o-', color=\"r\",\n",
    "                 label=\"Training score\")\n",
    "    plt.semilogx(param_range, test_scores_mean, 'o-', color=\"g\",\n",
    "                 label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    print(\"Best test score: {:.4f}\".format(test_scores_mean[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test score: 0.7524\n"
     ]
    },
    {
     "data": {
      "image/png": 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aNQBMnjyZ6Oho7r333iJpPC8pD/Edm1966aUyl3PrrbdWuIyBVNa6GWOqt9p3ZM+bB+PHw9atoOr8Hz/eGV/J0tLSSExMJCUlhW7dupGens748ePp06cP3bp1Y8qUKZ60Z599NmvWrCEvL4+4uDgmTZpEjx49GDBgAHv27AHgj3/8I9OnT/eknzRpEv369aNz58588YXz4qysrCwuvfRSEhMTGTt2LH369PEEMW8TJ04kMTGR008/nfvvvx+AXbt2MXr0aE4//XR69OjBV199BcDjjz9OUlISSUlJPP3006Wu27vvvsuAAQPo1asXl112GVlZWZW+TY0xp17Na1FMmAA+KkaPFSvg2LGi47Kz4frr4bnnfM/Tsye4FfSJWr9+Pa+88gp9+vQBYNq0aTRs2JC8vDyGDBnC2LFjSUxMLDJPRkYGgwcPZtq0adx99928+OKLTJo0qUTeqsrKlStZvHgxU6ZM4b333uPpp5+mWbNmvPnmm3z33Xf06tWrxHy7d+9myZIlrF27FhHh4MGDgNNiGTZsGLfddht5eXlkZ2fz1VdfMW/ePFatWkVeXh79+vUjOTmZyMjIIuu2Z88epk2bxkcffURUVBRTp07lqaee4oEHHqjQdjPGBI/a16IoHiTKGn+S2rdv7wkSAPPnz6dXr1706tWLH3/8kXXr1pWYJzIykhEjRgDQu3dvtmzZ4jPvSy65pESazz77jMsvd15R3aNHD7p161ZivoYNGxISEsKNN97IokWLPB29paam8vvf/x6AOnXqUL9+fT777DMuvfRSIiMjiYmJ4eKLL+bTTz8tsW5ffPEF69at46yzzqJnz57Mmzev1HIbY6qXmteiKOuXf0KCc7qpuDZtIDW10ovj3dvmxo0beeqpp1i5ciVxcXFcddVVPm/HDA8P93wODQ0lLy/PZ95169YtM40vYWFhrF69mg8++IA33niDmTNn8v777wMndueP97qpKsOHD2fu3Lnlnt8YUz3UvhbF1KkQFVV0XFSUMz7ADh06RExMDPXr1yc9PZ2lS5dW+jIGDhzIggULAPj+++99tlgOHz7MoUOHGDVqFE8++STffvstAEOGDGHWrFkA5Ofnc+jQIQYNGsSiRYs4cuQImZmZvP322wwaNKhEnmeddRbLly9n8+bNgHOtZOPGjZW+fsaYUy9ggUJEXhSRPSLyQynTRURmiEiaiPxPREqeTA+ElBSYPdtpQYg4/2fPPqm7nsqrV69eJCYm0qVLF6655hoGDhxY6cu4/fbb2bFjB4mJiTzyyCMkJiYSGxtbJE1GRgYXXHABPXr0YPDgwTzxxBMA/OMf/2Dp0qV0796dPn36sH79evr168cVV1xB37596d+/PzfffDPdu3cvsdymTZvywgsvcNlll9GjRw/OOussfvrpp0pfP2PMCZo3DxIS6A29K5pFwN6ZLSLnAJnAK6qa5GP6SOB2YCRwJvCUqp5ZVr6dO3fW4s8b/Pjjj3Tt2rVSyl3d5eXlkZeXR0REBBs3buT8889n48aN1KlT884yBoLtS6Wz91FUQ4V3eWZn0wdYrVqhp0oDVnuo6icikuAnyWicIKLAChGJE5HmqpoeqDLVBpmZmQwdOpS8vDxUlWeffdaChDG1UVYW3H+/c1fnSarKGuQ04Bev4e3uuBKBQkTGA+MB4uPjSS120Tk2NpbDhw8HrKDVSWhoaIntY9um/I4ePVpi+xlHZmambZsqJLm5hB08SPjBg4QdOEB4Robz3x0Oy8gg/MABT5rQSuy3rFr81FTV2cBscE49FW/+/vjjj8TExFRByUxNExERwRlnnFHVxQhKduqpkuXnw759sGcP/Pqr89/7r/i4jAzf+YSFQZMmEB/v9DTRp8/x4b/9zVnGSarKQLEDaOU13NIdZ4wx1Y8qHDzou5L3Nbx3rzNPcSEh0LixU9E3aQK9eh2v+Js0Of5XOBwb69yY40urVp5rFCejKgPFYuA2EXkN52J2hl2fMMZUisroz03VOc9fnkq/8H9uru+8GjQ4XrF36QLnnFN6xd+wIYSGnvw2gOPr/OCDvp8fK6eABQoRmQ8kA41FZDvwJyAMQFVnAUtw7nhKA7KB6wJVFmNMLeJ1pw9wvD83gLFjy1/x79kDR474XkZ09PGKvVUr6N279Iq/cWPweoj2lEtJgZQUvhb5uqJZBPKupyvKmK5AcHaHWgG7du1iwoQJrFq1iri4OJo2bcr06dPp1KlTVRethISEBFavXk3jxo0566yzPB0Kehs3bhyjRo1i7NixpeYzZ84czj//fFq0aAHADTfcwN13312i7ypjTpmDB+Gee0qeasnOhquvhquu8j1feHjRSr5r19Ir/vj4kg/t1nDV4mJ2ZZv3/Twe/OhBtmVso3Vsa6YOnUpK94o/cKeqjBkzhmuvvZbXXnsNgO+++47du3cXCRR5eXlBd6uqryBRXnPmzCEpKckTKJ5//vnKKlalCsbtbk5Sdjb8+CP88EPRv+3bS59HFf7855IVf5MmEBNT+nl+U/u68Jj3/TzG/2c8WzO2oihbM7Yy/j/jmfd9xbsZX7ZsGWFhYdx0002ecT169GDQoEGkpqYyaNAgLrroIs8v7SeeeMLTbXdht+FZWVmep6WTkpJ4/fXXAZg0aZKnO/Di77gAmDVrFhMnTvQMz5kzh9tuuw2Aiy++mN69e9OtWzdmz57ts+zR0dGAE+xuu+02OnfuzHnnnefp2hxgypQp9O3bl6SkJMaPH4+qsnDhQlavXk1KSgo9e/bkyJEjJCcns3r1asDp/LB79+4kJSV5ujEvXN6DDz5Ijx496N+/P7t37y5RpuXLl3te/HTGGWd4bu997LHH6N69Oz169PD0prtmzRr69+/P6aefzpgxYzhw4AAAycnJTJgwgT59+vDUU0/x66+/cumll9K3b1/69u3L559/XvoXaoJHbi6sXQuvvw4PPQRjxkDHjs6pnz59YNw4ePpp2LULkpNh2jQnAPjSpo1zrv7GG2H0aDjrLOjQAerXtyBRlsKXzlSXv06dOmlx69at83y+8907dfBLg0v9q/toXWUyJf7qPlq31HnufPfOEsv09tRTT+mECRN8Tlu2bJlGRUXp5s2bVVV19erVmpSUpJmZmXr48GFNTEzUb775RhcuXKg33HCDZ76DBw/q3r17tVOnTlpQUKCqqgcOHCiR/549e7R9+/ae4eHDh+unn36qqqr79u1TVdXs7Gzt1q2b7t27V1VV27Rpo7/++quqqtarV09VVd98800977zzNC8vT3fs2KGxsbH6xhtvFMlHVfWqq67SxYsXq6rq4MGDddWqVZ5phcM7duzQVq1a6Z49ezQ3N1eHDBmiixYtUlVVwDP/xIkT9dFHHy2xTqNGjdLPPvtMVVUPHz6subm5umTJEh0wYIBmZWUVKVP37t01NTVVVVUfeughvfPOOz1lufnmmz15XnHFFZ7tsnXrVu3SpUuJ5aoW3ZdMUcuWLQtc5nl5qhs3qr71luqf/6x6+eWqSUmqYWGqTltANTRUtWtX1bFjVSdPVl24UHX9etXc3KJ5vfqqalTU8fnAGX711cCVvxoAVmsF691a1x4/lu+7O/HSxleGfv360bZtW8DpBnzMmDGenlcvueQSPv30U4YPH84999zD/fffz6hRoxg0aJCnK47rr7+eUaNGMWrUqBJ5x8fH065dO1asWEHHjh1Zv369pw+pGTNmsGjRIgB++eUXNm7cSKNGjXyW8ZNPPuGKK64gNDSUFi1acO6553qmLVu2jMcff5zs7Gz2799Pt27duPDCC0td31WrVpGcnEy8+8suJSWFTz75hIsvvpjw8HDPevTu3ZsPPvigxPwDBw7k7rvvJiUlhUsuuYSWLVvy4Ycfct111xHlnhtu2LAhGRkZHDx4kMGDBwNw7bXX8tvf/taTz2WXXeb5/OGHHxbpIPHQoUNkZmZ6WlTmFFGFHTtKnjJat67oheO2bSEpCS680PmflASdO4PbY7Jf3nf6VNJbLGu7Ghcopg/33814wvQEtmaUvE2sTWwbUselVmiZ3bp1Y+HChaVO9+6OuzSdOnXim2++YcmSJfzxj39k6NChPPzww6xcuZKPPvqIhQsX8o9//IMPPviA3r2dvr0uuugipkyZwuWXX86CBQvo0qULY8aMQURITU3lww8/5MsvvyQqKork5GSfXZqX5ejRo9xyyy2sXr2aVq1aMXny5ArlUygsLMzTlXlp3aNPmjSJCy64gCVLljBw4MAK97Lrvd0LCgpYsWIFERERFSu4OXF795YMCD/8UPTBsebNnSBw003HA0JionNq6WS4d/qYylHrrlFMHTqVqLCidyxEhUUxdWjFuxk/99xzOXbsWJHrAP/73/88L/jxNmjQIN566y2ys7PJyspi0aJFDBo0iJ07dxIVFcVVV13FxIkT+eabb8jMzCQjI4ORI0fy5JNP8t133xEaGsqaNWtYs2aN51WqY8aM4e2332b+/PmelxZlZGTQoEEDoqKiWL9+PStWrPC7Dueccw6vv/46+fn5pKens2zZMgBPUGjcuDGZmZlFAmJMTIzP7kH69evH8uXL2bt3L/n5+cyfP9/zq788Nm3aRPfu3bn//vvp27cv69evZ9iwYbz00ktku3ez7N+/n9jYWBo0aODZznPnzi11Oeeff77nNa6Az9fDmgo6dMh5c+TzzztvmDzvPGjWzLlWMGQI3H47LFjgPEh25ZXwzDOwfLkTSHbuhPffhyeegN/9Dvr1O/kgYSpdjWtRlKXw7qbKvOtJRFi0aBETJkzgscceIyIigoSEBKZPn86OHUUfNu/Vqxfjxo2jX79+gHNL6RlnnMHSpUuZOHEiISEhhIWFMXPmTA4fPszo0aM5evQoqurpDry4Bg0a0LVrV9atW+fJd/jw4cyaNYuuXbvSuXNn+vfv73cdxowZw8cff0xiYiKtW7dmwIABAMTFxXHjjTeSlJREs2bN6Nu3r2eecePGcdNNNxEZGcmXX37pGd+8eXOmTZvGkCFDUFUuuOACRo8eXe7tOX36dJYtW0ZISAjdunVjxIgR1K1blzVr1tCnTx/Cw8MZOXIkf/nLX3j55Ze56aabyM7Opl27drz00ks+85wxYwa33norp59+Onl5eZxzzjmed2+Y8gk5dgy+/fZ4y2DtWue/94NcUVHQrRuMHHm8hZCU5LQc7IJxtRWwbsYDxboZN4Fk+xKQlwcbN5Y4ZaRpaUhBgZMmLMx5wtg7GCQlOW+QDKl1JyqqBRH5WlX7lJ2ypFrXojDGuAoKnNZA8WsI69dDTo6TJiTEuYU0KYmt/fuTMGqUExA6dHCChakVLFAYU1OU1r+RqvOcQfGAsHat05dRodatnSAwfPjxFkKXLhAZCcCW1FQSrPfYWskChTE1ga/+jcaNg0cfdfou2r//eNomTZwgcP31Re80KvbKXGMK1ZhAoaqe2y6NqYhqcb1O1blbaONGSEs7/v/f/z5+uqhQXh5s2QLXXns8IHTr5gQKY05AjQgUERER7Nu3j0aNGlmwMBWiquzbty84nrNQdVoB3oHA+/+hQ8fThoQ4XVPk5DCvOzw4FLbFQusMmPoRpPyQA88+W3XrYmqEGhEoWrZsyfbt2/n111+ruiimGouIiKBly5anZmGqTjfWvoJBWlrJYJCQ4FxA7t/f+d+xo/O/bVsID2fekMaMP2sf2W5v1lvjYPyFQKOG2GNn5mTViEARFhbm6SLDmKChCrt3H6/8iwcE74cVQ0OPB4PCzuoKg0FCgud9BnkFeezO3E16Zjq7MjeS/v0npGem89chWWQXO3OWHQ4Tzs2hy86vad+wPXERcads1U3NUiMChTFVpjAY+DpFlJYGmZnH04aGOi2ADh3g7LOLBIPMFo1JP7qXXZm7SM9MJ/1wOrsyPyV93QLSV6Y74w+nszd7L0r5r6XsLThMn+ecW+cbRjakfYP2dGjYgfYN2tO+YXvPcLPoZnba1pTKAoUxZSm8vdT71JD3Z+9gUKeOJxgUnDOIve2akd4yjl3xkaTXK2DXkV9JP5zutgjWkJ6WTvq36WTlZpVYbFhIGM2im9EsuhkJcQkMaDmAZtHNaB7dnOYxzT2fm0Y3pdPTnXz2YdZuJ2+hAAAchElEQVQ8ujnPjHyGTQc2sWn/JjYd2MSK7StYsHYB+ZrvSRcVFkW7Bu18BpI2cW2oE2JVRW1WI57MNuakqUJ6eunXDLyeNzhaN5Rdia3Z1aEZ6a0bsqtZPdLjQkmPzGcXmaRnOaeGdmfuLlIZF6pft37RCr9eM5rHNKd5tFv5u0GgYWRDQqR8TzkXvmclO/f4m92iwqKYfeFsn93T5ObnsjVjK5v2byJtf5oTSLyCydG84x0/1gmpQ5vYNjSgAX3b9y0SSNo1aFei7zQTnE7myWwLFKZamzfzFh7cPJtt9fJpnRXK1HbjSbn5n74Tqzqd0BUPAhs3omkbOVhwhF3RkB4D6bGh7GrdgPQWMexqEE56tJJe5yi78jM4kJNRIusQCaFJvSaeAODrl3/h50BVrJX15sYCLSD9cLoncBQGkjXb1rAndw8Hjh4okr5FTIsSp7IKhxtGNqys1TMnyQKFqZXmzbyF8Ttmku3Vk0RULsyOu4aUPtdBWhp5Gzewe8tadqVvJP3gL+wKO0a6Gwx2xQjpjeuSHiPsCs/hmJT89R9RJ6Jkhe/1y7/wc3y9+Bp/eiY1NZXk5GT2H9nvaXls2r+JtANpnuGdh3cWmScuIu544HCDR+Fw85jm5W4xmZNngcLUSgkT67A12kflngud9sGuaPi1HqiPa7QNw+NoHntaiQq/eFCoX7e+XeR1FQYKf7Jzs9l8YHORQLLpgNMq2ZqxlbyC4+8fiagTQbsG7XwGkjaxbQgLtb6kKpN1Cmhql8xMNrz+T7bWKxkkAI7WgTZd+9M/vi3Nmranef3TirQCmtZrSt065XhTmjlhUWFRJDVJIqlJUolpeQV5bMvY5vO6yIebPyxyfSVUQmkd29oJHA06eE5rFf6vF+7/ZWCVdRrOOCxQmOohP5+9H7zNa+88xiu5q1nVvKDUpG2yQlk8+ctSp5uqUSekDu0atKNdg3YMaz+syDRVZVfmrhLXRTYd2MQb695g35F9RdI3i252vAVSLJAsTVvK+P8ev7C/NWMr4/8zHsCCRQVZoDBB7egPa/jP/EeYm/4e77Y8Sl5j6JnTiL93vJy6Bw5zX/orJa5RTG03vuoKbCpERJxTgDHNObv12SWmHzx6sOh1ETeQfPzzx7zy3StF80JKPGuSnZvNgx89aIGigixQmKBTsGc3n7/6/5j7wzwWNN1LRgS0aF6Xu+Iv5OrRD9G99fG37MXNrFf+u55MtRUXEUfvFr3p3aJ3iWlHco/w88GfPYHkrqV3+cxja8ZWFv24iKHthlK/bv1AF7lGsYvZJjgcO8bGfz/H3OVPMzfyJ7bEQb28EC6J7MU1v7mPIWdcQmhIaFWXslYrz8XsYJAwPcHnw4eFLY06IXUY1HoQIzqMYGTHkSTGJ9aKGxbsYrapnlTZ9+n7vP72VF7J+oKvmucT0hSGFrTh0T6/5+JhtxMdHl3VpTTVzNShU30+fDjzgpm0iW3Dko1LeDftXe778D7u+/A+WtVvxciOIxnRYQRD2w21fc4Ha1GYU+7Ypg3891+Tmbv1PyxpkUVuKJx+LI6rO47lyosfokWD1lVdRONDdWlRQPnuetp+aDvvbnyXd9Pe5YPNH5CZk0lYSBjntDnH09ro0rhLjWlt2HMUJuhpRgZf/Gsac799mdcbpnMwEpodCyel8RCuHv0QPdoPrOoimjJUp0BxonLyc/h82+ee1sbaX9cC0Ca2jae1cW7bc8u8LTeYWaAwwSkvj7R35vLqR08wN2QtmxsoUXnCmLDTuXroXQwdkFLjn2auSWpyoChuW8Y2T2vjw80fkpWbRXhoOIPbDPa0Njo16lStWhsWKExQ2f/1Z7y+8BHmHlzOl81yEYWhOS25us/vGDPyHmIi7I6T6qg2BQpvx/KO8dm2z3g37V2WbFzCj3t/BKBtXFtPa2NI2yFB3zli0AYKERkOPAWEAs+r6rRi01sDLwNxbppJqrrEX54WKILTsZ3bWPLqn5i7aRH/bZpBbih0OxLDNW0v5spL/kTL+PZVXURzkmproChuy8EtntbGRz9/RHZuNnVD65KckOxpbXRs1LGqi1lCUAYKEQkFfgKGAduBVcAVqrrOK81s4FtVnSkiicASVU3wl68FiuCh2dmsWPB35q58ntfqb+NAJDQ9Wocr4wZx9UV/pGeXIdWqaW78s0BR0tG8o3y69VNPa2PDPqduat+gvae1kZyQTGRYZBWXNHhvj+0HpKnqZgAReQ0YDazzSqNA4XmIWKBo15Mm+Kiy+cM3mPveY7yav4a0BgVENhIulkSuGXQn5yX/zq47mFojok4Ew9oPY1j7YTzxmyfYfGCzp7Xx/DfP8/TKp4moE8GQhCGe1kb7htWvdR3IFsVYYLiq3uAOXw2cqaq3eaVpDrwPNADqAeep6tc+8hoPjAeIj4/vvWDBgoCU2ZQuZ9tPrF71Ev/N/ZovmzvXHQZmNGRY02H0PuNK6tmTrjVeZmYm0dH2jEF55RTksObgGlbuX8lX+79i+5HtALSMbMmZDc+kX8N+9IzrSXhI+Ckpz5AhQ4Ly1FN5AsXdbhn+LiIDgBeAJFUttcc3O/V06uTs28O78yYzd/0C/tNoHzl1IDGrHle3GknKpVNo1aJLVRfRnEJ26unkpO1P87Q2lm1ZxtG8o0TWieTctud6WhttG7QN2PKD9dTTDqCV13BLd5y364HhAKr6pYhEAI2BPQEsl/FDc3JYuehp5n4+k9ciN7EvCprEhnJz3YFcPeoBevUYYdcdjKmADg07cPuZt3P7mbdzJPcIqVtSPdc23tn4DrwLnRt19lzbOKfNOUHTHX4gWxR1cC5mD8UJEKuAK1V1rVead4HXVXWOiHQFPgJOUz+FshZFAKjy8+fv8Op//8KrR1fyU4N8IvJgdH5Hrj77Vs4//2bC6pya5rEJXtaiCJyN+zZ6HvZL3ZLKsfxjRIVFMbTtUEZ0GMGIjiNIiEs4qWUE5V1PACIyEpiOc+vri6o6VUSmAKtVdbF7p9NzQDTOhe37VPV9f3laoKg8B3/+kTdee4i5O97l03inX5zkzHiuTrycS8c+TGxM4youoQkmFihOjezcbJb9vMzT2vj54M8AdG3c1dPaOLv12Sfc2gjaQBEIFihOTu6hg7z32qPM/X4ei+N2c6wOdMmM5Opmw0i5dAptEnpUdRFNkLJAceqpKj/t+8kTNJZvXU5Ofg71wupxXrvzPK2N1rFl948WrNcoTJDQ/HxWvzObV5bP4LU669kbBY2jQxgf0perf3M/fc68xK47GBOERITOjTvTuXFnJvSfQFZOFh///LEncLy94W0AusV387Q2BrYeSHjo8VPFhR0k0pySL/MobzmsRVFzbf3mY159awpzD3/Ohrg86ubBRTltuebM8fzmwrsICwuOC2WmerAWRXBRVdbvXe8JGp9s/YTcglxiwmM8rY2jeUeZ9NEkp8v1Z0F3aoV+EVqLoobJ2PkzC+c/xNyti1ne6DCEwjkhDbi36VjG/t9k4hq2qOoiGmMqgYjQNb4rXeO7cveAuzl87HCR1sai9YsqbVkWKKqheTNvKfL6zyltrqNR/abM/WYOb0fv4GgYdAoP59Hw4aRcMpm2nc+s6iIbYwIspm4Mo7uMZnSX0agq635dR9LMpErJ2wJFNTNv5i2M3zGTbPcB2a3R+Vy793nYB40ihOu1B1efcy/9klPsuoMxtZSI0K1JN9rEtvH5WtgTZYGimvnD5tmeIOEhEJ8tbJ+SSXjd4O7q2Bhz6vh6LWxFhFRSeUyAbVv7BZMePJNf6uX7nL43Ui1IGGOKSOmewuwLZ9Mmts1J5WMtiiCmBQV8uvhpZix/nEX1d0IdiMyDI2El07bOCj31BTTGBL2U7imkdE9B7pISHa6Wl7UogtCRjH28+NQ4zrinHoO/m8CyuulMLOjP5su/4LnTbiYqt2j6qFyY2m581RTWGFPjWYsiiPyybgX//NddPJf3Ffsile4SwXMNruXKu/5OVP1GALTpOgBmUuSup6ntxpNy8z+ruPTGmJrKAkUV04ICPvvvM8xIfYxFMTvQUBid3YI7BtzH4AtvR0JKNvpSbv4nKVhgMMacGhYoqsjRwweY/9I9zPh5PmvijtKgrnBP/pnccuUTtEk8q6qLZ4wxHhYoTrHt61cyc94EZueuYG+kkiR1mR13NSnjniAq1nprNcYEHwsUp4AWFPD5OzOZsWwa/47ZjobARTnNuePMiSSPvtPn6SVjjAkWFigC6GjmQV576R5mbJ7Pt3FHiKsr3JXfl1suf4K2SWdXdfGMMaZcLFAEwI4Nq5g5bwLP5nzJ3kilm9RlVmwKV935JPXi4qu6eMYYc0IsUFQSLSjgiyWzePrjabwZ/Qv5IXBRTjPu6HcvQy6+y04vGWOqLQsUJ+lo5kFen3MvMzb9i2/ijhAbAXfm9+GWsX+j3emDq7p4xhhz0ixQVNCOn1Yz69UJPHvsC36NUrpKODPrX8lV454gukHTqi6eMcZUGgsUJ0ALCljx3vPM+HAqC6O3kR8CF+Y0445+d3PumHvs9JIxpkayQFEOx7IOsWDORGakvcrquGxi68Id+b259dK/0a5HclUXzxhjAsoChR87N37DrFfv5Nmjn7MnSukSEs4/Yy7n6tufJLphs6ounjHGnBIWKIrRggK+WvoCMz74M2+4p5cuyGnKHX3v4rxLJtrpJWNMrWOBwnUs6xBvvHwfMzbOZVVcNvUj4Pa8Xtz6f3+lfc9zq7p4xhhTZWp9oEjftIZn597JrCOfsTuqgM4h4TwTfRnX3D7dTi8ZYwy1OFB8tfQFZrz/KG/U20puKFyQ04Q7+kzgvEvvIyTE3hZnjDGFalWgyMk+7Jxe+mkuK+OyqF8Xbsnrya0X/5WOvc6r6uIZY0xQqhWBYtfm//HsK3cwK/tTdtUroFNIGP+o91uuuW06MY1aVHXxjDEmqNXoQLHq/TnMWPoIr9fbQm4ojMyN544udzJs7P2EhNboVTfGmEpT42rLnCOZLHz5fmZseJmv4rKIqQs35/bkttGP07H3sKounjHGVDsBDRQiMhx4CggFnlfVaT7S/B8wGVDgO1W9siLL2v3zDzz7yh3MzFrOrnoFdAwJY0bUWK69dTr1G592EmthjDG1W8AChYiEAs8Aw4DtwCoRWayq67zSdAT+AAxU1QMi0uREl7P6g5eZsXQKr0duJqcOjMiN5/bOt/Ob3/7BTi8ZY0wlKHdNKiJnAx1V9SURiQeiVfVnP7P0A9JUdbM7/2vAaGCdV5obgWdU9QCAqu4pqxw/Hf6JNhPrMCqkK98e28KXDTKJrgu/zz2d20Y/Tqc+vynvKhljjCmHcgUKEfkT0AfoDLwEhAGvAgP9zHYa8IvX8HbgzGJpOrn5f45zemqyqr7nY/njgfEANIdt0fn8kx9oUgB/zBzEwEG3E1E/np2ZsDM1tTyrZIw5QZmZmaTa8VUrlbdFMQY4A/gGQFV3ikhMJS2/I5AMtAQ+EZHuqnrQO5GqzgZmA0gL0cLxEYTy6F8/qYRiGGPKkpqaSnJyclUXw1SB8vZwl6OqinPBGRGpV455dgCtvIZbuuO8bQcWq2quexrrJ5zAUS6/1Msvb1JjjDEVVN5AsUBEngXiRORG4EPguTLmWQV0FJG2IhIOXA4sLpbmLZzWBCLSGOdU1OZylonWWdbVhjHGBFq5Tj2p6t9EZBhwCOc6xcOq+kEZ8+SJyG3AUpzrDy+q6loRmQKsVtXF7rTzRWQdkA9MVNV95SlTVC5MbTe+PEmNMcachDIDhXub64eqOgTwGxyKU9UlwJJi4x72+qzA3e5fubXJDGVqu/Gk3PzPE5nNGGNMBZQZKFQ1X0QKRCRWVTNORaH86RTTiQ1/3VDVxTDGmFqjvHc9ZQLfi8gHQFbhSFW9IyClMsYYEzTKGyj+7f4ZY4ypZcp7Mftl986lTu6oDaqaG7hiGWOMCRblfTI7GXgZ2AII0EpErlVVe9rNGGNquPKeevo7cL6qbgAQkU7AfKB3oApmjDEmOJT3gbuwwiABoKo/4fT3ZIwxpoYrb4titYg8j9MRIEAKsDowRTLGGBNMyhsobgZuBQpvh/0UsKfdjDGmFihvoKgDPKWqT4Dnae26ASuVMcaYoFHeaxQfAZFew5E4HQMaY4yp4cobKCJUNbNwwP0cFZgiGWOMCSblDRRZItKrcEBE+gBHAlMkY4wxwaS81ygmAG+IyE53uDlwWWCKZIwxJpj4bVGISF8Raaaqq4AuwOtALvAe8PMpKJ8xxpgqVtapp2eBHPfzAOAB4BngAO47rI0xxtRsZZ16ClXV/e7ny4DZqvom8KaIrAls0YwxxgSDsloUoSJSGEyGAh97TSvv9Q1jjDHVWFmV/XxguYjsxbnL6VMAEekAVPnb7owxxgSe30ChqlNF5COcu5zed99xDU5L5PZAF84YY0zVK887s1f4GPdTYIpjjDEm2JT3gTtjjDG1lAUKY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxflmgMMYY45cFCmOMMX4FNFCIyHAR2SAiaSIyyU+6S0VE3VesGmOMCSIBCxQiEorzkqMRQCJwhYgk+kgXA9wJfBWoshhjjKm4QLYo+gFpqrpZVXOA14DRPtI9CjwGHA1gWYwxxlRQIF8+dBrwi9fwduBM7wQi0gtoparviMjE0jISkfHAeID4+HhSU1Mrv7TGGL8yMzPt2KulquwtdSISAjwBjCsrrarOxn1Hd+fOnTU5OTmgZTPGlJSamoode7VTIE897QBaeQ23dMcVigGSgFQR2QL0BxbbBW1jjAkugQwUq4COItJWRMKBy4HFhRNVNUNVG6tqgqomACuAi1R1dQDLZIwx5gQFLFCoah5wG7AU+BFYoKprRWSKiFwUqOUaY4ypXAG9RqGqS4AlxcY9XEra5ECWxRhjTMXYk9nGGGP8skBhjDHGLwsUxhhj/LJAYYwxxi8LFMYYY/yyQGGMMcYvCxTGGGP8skBhjDHGLwsUxhhj/LJAYYwxxi8LFMYYY/yyQGGMMcYvCxTGGGP8skBhjDHGLwsUxhhj/LJAYYwxxi8LFMYYY/yyQGGMMcYvCxTGGGP8skBhjDHGLwsUxhhj/LJAYYwxxi8LFMYYY/yyQGGMMcYvCxTGGGP8skBhjDHGLwsUxhhj/LJAYYwxxi8LFMYYY/yyQGGMMcavgAYKERkuIhtEJE1EJvmYfreIrBOR/4nIRyLSJpDlMcYYc+ICFihEJBR4BhgBJAJXiEhisWTfAn1U9XRgIfB4oMpjjDGmYgLZougHpKnqZlXNAV4DRnsnUNVlqprtDq4AWgawPMYYYyogkIHiNOAXr+Ht7rjSXA+8G8DyGGOMqYA6VV0AABG5CugDDC5l+nhgPEB8fDypqamnrnDGGAAyMzPt2KulAhkodgCtvIZbuuOKEJHzgAeBwap6zFdGqjobmA3QuXNnTU5OrvTCGmP8S01NxY692imQp55WAR1FpK2IhAOXA4u9E4jIGcCzwEWquieAZTHGGFNBAQsUqpoH3AYsBX4EFqjqWhGZIiIXucn+CkQDb4jIGhFZXEp2xhhjqkhAr1Go6hJgSbFxD3t9Pi+QyzfGGHPy7MlsY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxflmgMMYY45cFCmOMMX5ZoDDGGOOXBQpjjDF+WaAwxhjjlwUKY4wxfgU0UIjIcBHZICJpIjLJx/S6IvK6O/0rEUkIZHmMMcacuIAFChEJBZ4BRgCJwBUiklgs2fXAAVXtADwJPBao8hhjjKmYQLYo+gFpqrpZVXOA14DRxdKMBl52Py8EhoqIBLBMxhhjTlCdAOZ9GvCL1/B24MzS0qhqnohkAI2Avd6JRGQ8MN4dPCYiPwSkxNVPLJBR1YUoxakuW6CWVxn5nkweFZn3ROY5kbSNKXZs1lLBfNxB6eVrU9EMAxkoKo2qzgZmA4jIalXtU8VFCgoiMltVx5ed8tQ71WUL1PIqI9+TyaMi857IPCeY1o49gvu4g8CUL5CnnnYArbyGW7rjfKYRkTo4kXBfAMtU0/ynqgvgx6kuW6CWVxn5nkweFZn3ROYJ5n0oWAX7Nqv08omqVnaeTsZOxf8TMBQnIKwCrlTVtV5pbgW6q+pNInI5cImq/l8Z+dqvGmOqgB17tVfATj251xxuA5YCocCLqrpWRKYAq1V1MfACMFdE0oD9wOXlyHp2oMpsjPHLjr1aKmAtCmOMMTWDPZltjDHGLwsUxhhj/LJAYYwxxq9qHyhEpJ6IvCwiz4lISlWXx5jaQETaicgLIrKwqstiAi8oA4WIvCgie4o/gV1KJ4OXAAtV9UbgolNeWGNqiBM57tyuea6vmpKaUy0oAwUwBxjuPcJPJ4MtOd5VSP4pLKMxNc0cyn/cmVokKAOFqn6C81yFt9I6GdyOEywgSNfHmOrgBI87U4tUp4rVVyeDpwH/Bi4VkZkE/6P1xlQ3Po87EWkkIrOAM0TkD1VTNHOqVItOAf1R1SzguqouhzG1iaruA26q6nKYU6M6tSjK08mgMaZy2XFnqlWgWAV0FJG2IhKO0y/U4ioukzE1nR13JjgDhYjMB74EOovIdhG5XlXzgMJOBn8EFnj3RGuMOTl23JnSWKeAxhhj/ArKFoUxxpjgYYHCGGOMXxYojDHG+GWBwhhjjF8WKIwxxvhlgcIYY4xfFiiMMcb4ZYHCmAARkS0i0riC844TkRaVkZcxJ8sChTHBaRzQoqxExpwKFihMjSciCSKyXkTmiMhPIjJPRM4Tkc9FZKOI9HP/vhSRb0XkCxHp7M57l4i86H7uLiI/iEhUKctpJCLvi8haEXkeEK9pV4nIShFZIyLPui8EQkQyReRJd56PRCReRMYCfYB5bvpIN5vbReQbEfleRLoEcpsZ480ChaktOgB/B7q4f1cCZwP3Ag8A64FBqnoG8DDwF3e+p4AOIjIGeAn4vapml7KMPwGfqWo3YBHQGkBEugKXAQNVtSfOmxgL3+9eD1jtzrMc+JOqLgRWAymq2lNVj7hp96pqL2CmW25jTolq/z4KY8rpZ1X9HkBE1gIfqaqKyPdAAhALvCwiHQEFwgBUtUBExgH/A55V1c/9LOMcnHe4o6rviMgBd/xQoDewSkQAIoE97rQC4HX386s4L+IqTeG0rwuXY8ypYIHC1BbHvD4XeA0X4BwHjwLLVHWMiCQAqV7pOwKZVPyagQAvq2p53gTnr5fOwjLnY8euOYXs1JMxjliOv5BnXOFIEYkFZuC0Fhq51w9K8wnOKS1EZATQwB3/ETBWRJq40xqKSBt3WghQmOeVwGfu58NAzEmsjzGVxgKFMY7Hgf8nIt9S9Nf6k8AzqvoTcD0wrbDC9+ER4Bz31NYlwDYAVV0H/BF4X0T+B3wANHfnyQL6icgPwLnAFHf8HGBWsYvZxlQJex+FMVVIRDJVNbqqy2GMP9aiMMYY45e1KIw5QSJyHXBnsdGfq+qtVVEeYwLNAoUxxhi/7NSTMcYYvyxQGGOM8csChTHGGL8sUBhjjPHLAoUxxhi//j8vaq9jWkdCngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=8)\n",
    "param_name = 'max_depth'\n",
    "param_range = [1, 2, 4, 8, 16, 32]\n",
    "\n",
    "plot_validation_curve(clf, X_train, y_train,\n",
    "                      param_name, param_range, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosted Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC Random Forest: 0.9142 +/-0.0029\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=30, max_features=10,\n",
    "                             max_depth=10)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n",
    "                         n_jobs=-1)\n",
    "print(\"ROC Random Forest: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Gradient Boosted Trees: 0.9183 +/-0.0030\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "clf = GradientBoostingClassifier(max_leaf_nodes=5, learning_rate=0.1,\n",
    "                                 n_estimators=100)\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n",
    "                         n_jobs=-1)\n",
    "print(\"ROC AUC Gradient Boosted Trees: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.82 s, sys: 0 ns, total: 1.82 s\n",
      "Wall time: 1.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "_ = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9164\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "y_pred_proba = clf.predict_proba(X_test)[:, 1]\n",
    "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.88      0.95      0.91      4918\n",
      "        >50K       0.78      0.59      0.67      1595\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      6513\n",
      "   macro avg       0.83      0.77      0.79      6513\n",
      "weighted avg       0.85      0.86      0.85      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision_gb, recall_gb, _ = precision_recall_curve(y_test, y_pred_proba)\n",
    "\n",
    "plt.plot(precision_gb, recall_gb)\n",
    "plt.xlabel('precision')\n",
    "plt.ylabel('recall')\n",
    "plt.title('Precision / Recall curve');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "ordering = np.argsort(clf.feature_importances_)[::-1]\n",
    "\n",
    "importances = clf.feature_importances_[ordering]\n",
    "feature_names = features.columns[ordering]\n",
    "\n",
    "x = np.arange(len(feature_names))\n",
    "plt.bar(x, importances)\n",
    "plt.xticks(x + 0.5, feature_names, rotation=90, fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the boosted trees to extract features for a Logistic Regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is an implementation of a trick found in:\n",
    "\n",
    "Practical Lessons from Predicting Clicks on Ads at Facebook\n",
    "Junfeng Pan, He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela\n",
    "International Workshop on Data Mining for Online Advertising (ADKDD)\n",
    "\n",
    "https://www.facebook.com/publications/329190253909587/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin, clone\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "\n",
    "class TreeTransform(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"One-hot encode samples with an ensemble of trees\n",
    "    \n",
    "    This transformer first fits an ensemble of trees (e.g. gradient\n",
    "    boosted trees or a random forest) on the training set.\n",
    "\n",
    "    Then each leaf of each tree in the ensembles is assigned a fixed\n",
    "    arbitrary feature index in a new feature space. If you have 100\n",
    "    trees in the ensemble and 2**3 leafs per tree, the new feature\n",
    "    space has 100 * 2**3 == 800 dimensions.\n",
    "    \n",
    "    Each sample of the training set go through the decisions of each tree\n",
    "    of the ensemble and ends up in one leaf per tree. The sample if encoded\n",
    "    by setting features with those leafs to 1 and letting the other feature\n",
    "    values to 0.\n",
    "    \n",
    "    The resulting transformer learn a supervised, sparse, high-dimensional\n",
    "    categorical embedding of the data.\n",
    "    \n",
    "    This transformer is typically meant to be pipelined with a linear model\n",
    "    such as logistic regression, linear support vector machines or\n",
    "    elastic net regression.\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, estimator):\n",
    "        self.estimator = estimator\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        self.fit_transform(X, y)\n",
    "        return self\n",
    "        \n",
    "    def fit_transform(self, X, y):\n",
    "        self.estimator_ = clone(self.estimator)\n",
    "        self.estimator_.fit(X, y)\n",
    "        self.binarizers_ = []\n",
    "        sparse_applications = []\n",
    "        estimators = np.asarray(self.estimator_.estimators_).ravel()\n",
    "        for t in estimators:\n",
    "            lb = LabelBinarizer(sparse_output=True)\n",
    "            X_leafs = t.tree_.apply(X.astype(np.float32))\n",
    "            sparse_applications.append(lb.fit_transform(X_leafs))\n",
    "            self.binarizers_.append(lb)\n",
    "        return hstack(sparse_applications)\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        sparse_applications = []\n",
    "        estimators = np.asarray(self.estimator_.estimators_).ravel()\n",
    "        for t, lb in zip(estimators, self.binarizers_):\n",
    "            X_leafs = t.tree_.apply(X.astype(np.float32))\n",
    "            sparse_applications.append(lb.transform(X_leafs))\n",
    "        return hstack(sparse_applications)\n",
    "\n",
    "\n",
    "boosted_trees = GradientBoostingClassifier(\n",
    "    max_leaf_nodes=5, learning_rate=0.1,\n",
    "    n_estimators=100, random_state=0,\n",
    ") \n",
    "\n",
    "pipeline = make_pipeline(\n",
    "    TreeTransform(boosted_trees),\n",
    "    LogisticRegression(C=1)\n",
    ")\n",
    "\n",
    "pipeline.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9233\n"
     ]
    }
   ],
   "source": [
    "y_pred_proba = pipeline.predict_proba(X_test)[:, 1]\n",
    "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.89      0.93      0.91      4918\n",
      "        >50K       0.76      0.66      0.70      1595\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      6513\n",
      "   macro avg       0.82      0.79      0.81      6513\n",
      "weighted avg       0.86      0.86      0.86      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipeline.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision_gb_lr, recall_gb_lr, _ = precision_recall_curve(\n",
    "    y_test, y_pred_proba)\n",
    "\n",
    "plt.plot(precision_gb, recall_gb, label='GBT')\n",
    "plt.plot(precision_gb_lr, recall_gb_lr, label='GBT + LR')\n",
    "plt.xlabel('precision')\n",
    "plt.ylabel('recall')\n",
    "plt.title('Precision / Recall curve')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-validation of a complete pipeline from the raw features\n",
    "\n",
    "In this final example, we will fit a simpler baseline model (logistic regression) wrapped into a pipeline that includes both feature engineering and supervised learning in one go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91093744, 0.91035292, 0.90915075, 0.90166315, 0.90143792])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler, QuantileTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "pipeline = make_pipeline(\n",
    "    make_column_transformer(\n",
    "        (categorical_features, OneHotEncoder(handle_unknown='ignore')),\n",
    "        (numeric_features, StandardScaler()),\n",
    "    ),\n",
    "    LogisticRegression(C=1000),\n",
    ")\n",
    "\n",
    "cv_scores = cross_val_score(pipeline, features_train, y_train,\n",
    "                            scoring='roc_auc', cv=5, n_jobs=-1)\n",
    "cv_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Logistic Regression pipeline: 0.9067 +/-0.0043\n"
     ]
    }
   ],
   "source": [
    "print(\"ROC AUC Logistic Regression pipeline: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(cv_scores), np.std(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrain the same pipeline on the full training set for final evaluation on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venvs/py36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/opt/venvs/py36/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venvs/py36/lib/python3.6/site-packages/sklearn/pipeline.py:605: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  res = transformer.transform(X)\n"
     ]
    }
   ],
   "source": [
    "pipeline.fit(features_train, y_train)\n",
    "y_pred_proba = pipeline.predict_proba(features_test)[:, 1]\n",
    "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final test score is in-line with the results of cross-validation done on the training set."
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